Report of French Employment, Salaries, Population

Table of Contents

• 1. Basic info
• 2 Data loading and cleaning
  • Section 1.1
  • Section 1.2
• 3. Data analysis
  • 3.1. Current French labour market information
    • 3.1.1. The unemployment rate
    • 3.1.2. Mean salary for France
    • 3.1.3. Mean salary by working position
    • 3.1.4. Mean salary by age
  • 3.2. Labour situation by regions
    • 3.2.1. Mean salary per region
    • 3.2.2. Regions with the lowest and highest salaries
    • 3.2.3. Salary per region and gender
  • 3.3. Labour situation by cities
    • 3.3.1. Mean salary per town
    • 3.3.2. Mean salary per town and gender
    • 3.3.3. Mean salary per town and age

1. Basic info

Dataset is coming from INSEE. INSEE is the official french institute gathering data of many types around France. It can be demographic (Births, Deaths, Population Density…), Economic (Salary, Firms by activity / size…) and more. Data and descriptin can be found at this link. Four files are in the dataset:

baseetablissementpartrancheeffectif : give information on the number of firms in every french town, categorized by size , come from INSEE.

• CODGEO : geographique code for the town (can be joined with codeinsee column from "namegeographic_information.csv')
• LIBGEO : name of the town (in french)
• REG : region number
• DEP : depatment number
• E14TST : total number of firms in the town
• E14TS0ND : number of unknown or null size firms in the town
• E14TS1 : number of firms with 1 to 5 employees in the town
• E14TS6 : number of firms with 6 to 9 employees in the town
• E14TS10 : number of firms with 10 to 19 employees in the town
• E14TS20 : number of firms with 20 to 49 employees in the town
• E14TS50 : number of firms with 50 to 99 employees in the town
• E14TS100 : number of firms with 100 to 199 employees in the town
• E14TS200 : number of firms with 200 to 499 employees in the town
• E14TS500 : number of firms with more than 500 employees in the town

namegeographicinformation : give geographic data on french town (mainly latitude and longitude, but also region / department codes and names )

• EU_circo : name of the European Union Circonscription
• code_région : code of the region attached to the town
• nom_région : name of the region attached to the town
• chef.lieu_région : name the administrative center around the town
• numéro_département : code of the department attached to the town
• nom_département : name of the department attached to the town
• préfecture : name of the local administrative division around the town
• numéro_circonscription : number of the circumpscription
• nom_commune : name of the town
• codes_postaux : post-codes relative to the town
• code_insee : unique code for the town
• latitude : GPS latitude
• longitude : GPS longitude
• éloignement

netsalarypertownper_category : salaries around french town per job categories, age and sex

• CODGEO : unique code of the town
• LIBGEO : name of the town
• SNHM14 : mean net salary
• SNHMC14 : mean net salary per hour for executive
• SNHMP14 : mean net salary per hour for middle manager
• SNHME14 : mean net salary per hour for employee
• SNHMO14 : mean net salary per hour for worker
• SNHMF14 : mean net salary for women
• SNHMFC14 : mean net salary per hour for feminin executive
• SNHMFP14 : mean net salary per hour for feminin middle manager
• SNHMFE14 : mean net salary per hour for feminin employee
• SNHMFO14 : mean net salary per hour for feminin worker
• SNHMH14 : mean net salary for man
• SNHMHC14 : mean net salary per hour for masculin executive
• SNHMHP14 : mean net salary per hour for masculin middle manager
• SNHMHE14 : mean net salary per hour for masculin employee
• SNHMHO14 : mean net salary per hour for masculin worker
• SNHM1814 : mean net salary per hour for 18-25 years old
• SNHM2614 : mean net salary per hour for 26-50 years old
• SNHM5014 : mean net salary per hour for >50 years old
• SNHMF1814 : mean net salary per hour for women between 18-25 years old
• SNHMF2614 : mean net salary per hour for women between 26-50 years old
• SNHMF5014 : mean net salary per hour for women >50 years old
• SNHMH1814 : mean net salary per hour for men between 18-25 years old
• SNHMH2614 : mean net salary per hour for men between 26-50 years old
• SNHMH5014 : mean net salary per hour for men >50 years old

population : demographic information in France per town, age, sex and living mode

• <NIVGEO : geographic level (arrondissement, communes…)
• CODGEO : unique code for the town
• LIBGEO : name of the town (might contain some utf-8 errors, this information has better quality namegeographicinformation)
• MOCO : cohabitation mode : [list and meaning available in Data description]
• AGE80_17 : age category (slice of 5 years) | ex : 0 -> people between 0 and 4 years old
• SEXE : sex, 1 for men | 2 for women
• NB : number of people in the category

departments.geojson : contains the borders of french departments. From Gregoire David (github)

This report is using only netsalarypertownper_category and name_geographic_information parts. Other files are described for further analysis purposes if needed. These datasets can be merged by : CODGEO = code_insee.

2. Data loading and cleaning

	unique code of the town	name of the town	mean net salary	mean net salary per hour for executive	mean net salary per hour for middle manager	mean net salary per hour for employee	mean net salary per hour for worker	mean net salary for women	mean net salary per hour for feminin executive	mean net salary per hour for feminin middle manager	...	mean net salary per hour for masculin worker	mean net salary per hour for 18-25 years old	mean net salary per hour for 26-50 years old	mean net salary per hour for >50 years old	mean net salary per hour for women between 18-25 years old	mean net salary per hour for women between 26-50 years old	mean net salary per hour for women >50 years old	mean net salary per hour for men between 18-25 years old	mean net salary per hour for men between 26-50 years old	mean net salary per hour for men >50 years old
0	01004	Ambérieu-en-Bugey	13.7	24.2	15.5	10.3	11.2	11.6	19.1	13.2	...	11.6	10.5	13.7	16.1	9.7	11.8	12.5	11.0	14.9	18.6
1	01007	Ambronay	13.5	22.1	14.7	10.7	11.4	11.9	19.0	13.3	...	11.7	9.8	13.8	14.6	9.2	12.2	12.5	10.2	14.9	16.4
2	01014	Arbent	13.5	27.6	15.6	11.1	11.1	10.9	19.5	11.7	...	11.8	9.3	13.3	16.0	8.9	10.6	12.5	9.6	15.1	18.6
3	01024	Attignat	12.9	21.8	14.1	11.0	11.3	11.4	19.0	13.0	...	11.6	9.6	12.9	14.2	9.3	11.4	12.2	9.7	13.8	15.9
4	01025	Bâgé-la-Ville	13.0	22.8	14.1	10.5	11.1	11.6	19.4	13.6	...	11.4	9.4	12.8	15.2	9.0	11.8	12.3	9.7	13.4	16.9
5	01027	Balan	13.9	22.2	15.1	11.0	11.4	12.5	20.3	14.0	...	11.7	9.7	14.1	15.4	9.5	12.8	13.0	9.9	15.3	17.2
6	01031	Bellignat	12.4	24.0	13.1	10.5	10.4	10.9	20.7	11.8	...	10.8	9.3	12.5	13.3	8.9	11.0	11.5	9.6	13.3	14.9
7	01032	Béligneux	14.0	23.1	15.3	10.9	11.3	12.4	20.5	13.9	...	11.6	9.7	13.9	16.7	9.7	12.4	13.8	9.6	15.0	19.3
8	01033	Bellegarde-sur-Valserine	11.5	21.2	13.5	9.9	10.5	10.3	20.8	12.3	...	11.0	9.6	11.5	12.7	9.2	10.3	11.3	10.0	12.3	13.9
9	01034	Belley	12.4	23.4	14.1	10.3	10.5	11.0	21.5	13.0	...	10.9	9.7	12.3	13.7	9.3	11.2	11.4	9.9	13.0	15.4

10 rows × 26 columns

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5136 entries, 0 to 5135
Data columns (total 26 columns):
 #   Column                                                      Non-Null Count  Dtype  
---  ------                                                      --------------  -----  
 0   unique code of the town                                     5136 non-null   object 
 1   name of the town                                            5136 non-null   object 
 2   mean net salary                                             5136 non-null   float64
 3   mean net salary per hour for executive                      5136 non-null   float64
 4   mean net salary per hour for middle manager                 5136 non-null   float64
 5   mean net salary per hour for employee                       5136 non-null   float64
 6   mean net salary per hour for worker                         5136 non-null   float64
 7   mean net salary for women                                   5136 non-null   float64
 8   mean net salary per hour for feminin executive              5136 non-null   float64
 9   mean net salary per hour for feminin middle manager         5136 non-null   float64
 10  mean net salary per hour for feminin employee               5136 non-null   float64
 11  mean net salary per hour for feminin worker                 5136 non-null   float64
 12  mean net salary for man                                     5136 non-null   float64
 13  mean net salary per hour for masculin executive             5136 non-null   float64
 14  mean net salary per hour for masculin middle manager        5136 non-null   float64
 15  mean net salary per hour for masculin employee              5136 non-null   float64
 16  mean net salary per hour for masculin worker                5136 non-null   float64
 17  mean net salary per hour for 18-25 years old                5136 non-null   float64
 18  mean net salary per hour for 26-50 years old                5136 non-null   float64
 19  mean net salary per hour for >50 years old                  5136 non-null   float64
 20  mean net salary per hour for women between 18-25 years old  5136 non-null   float64
 21  mean net salary per hour for women between 26-50 years old  5136 non-null   float64
 22  mean net salary per hour for women >50 years old            5136 non-null   float64
 23  mean net salary per hour for men between 18-25 years old    5136 non-null   float64
 24  mean net salary per hour for men between 26-50 years old    5136 non-null   float64
 25  mean net salary per hour for men >50 years old              5136 non-null   float64
dtypes: float64(24), object(2)
memory usage: 1.0+ MB

	mean net salary	mean net salary per hour for executive	mean net salary per hour for middle manager	mean net salary per hour for employee	mean net salary per hour for worker	mean net salary for women	mean net salary per hour for feminin executive	mean net salary per hour for feminin middle manager	mean net salary per hour for feminin employee	mean net salary per hour for feminin worker	...	mean net salary per hour for masculin worker	mean net salary per hour for 18-25 years old	mean net salary per hour for 26-50 years old	mean net salary per hour for >50 years old	mean net salary per hour for women between 18-25 years old	mean net salary per hour for women between 26-50 years old	mean net salary per hour for women >50 years old	mean net salary per hour for men between 18-25 years old	mean net salary per hour for men between 26-50 years old	mean net salary per hour for men >50 years old
count	5136.000000	5136.000000	5136.000000	5136.000000	5136.000000	5136.000000	5136.000000	5136.000000	5136.000000	5136.000000	...	5136.000000	5136.000000	5136.000000	5136.00000	5136.000000	5136.000000	5136.000000	5136.000000	5136.000000	5136.000000
mean	13.706386	23.703836	14.575876	10.564505	11.235650	12.038026	20.220502	13.274260	10.308879	9.827161	...	11.498189	9.549766	13.495444	15.87722	9.161565	12.055160	13.174143	9.820872	14.485981	17.679809
std	2.559329	2.836183	1.490110	0.811775	1.221755	1.787995	2.327550	0.990166	0.747563	1.104684	...	1.289598	0.997444	2.363144	3.58586	0.453023	1.825306	2.249391	1.539949	2.852490	4.530257
min	10.200000	16.000000	11.600000	8.700000	8.300000	9.300000	12.000000	10.600000	8.700000	6.100000	...	8.900000	7.900000	9.700000	10.50000	7.500000	9.100000	9.500000	7.800000	9.600000	10.800000
25%	12.100000	21.900000	13.800000	10.000000	10.600000	10.900000	18.800000	12.600000	9.800000	9.200000	...	10.800000	9.200000	12.000000	13.70000	8.900000	10.900000	11.700000	9.400000	12.700000	14.900000
50%	13.000000	23.200000	14.400000	10.400000	11.000000	11.500000	20.000000	13.100000	10.100000	9.700000	...	11.300000	9.500000	12.900000	15.00000	9.100000	11.600000	12.600000	9.700000	13.800000	16.600000
75%	14.400000	24.900000	15.100000	10.900000	11.600000	12.700000	21.400000	13.800000	10.600000	10.200000	...	11.900000	9.700000	14.300000	16.90000	9.400000	12.700000	14.000000	10.000000	15.500000	19.000000
max	43.300000	51.500000	54.600000	17.500000	46.300000	26.700000	35.500000	19.000000	16.100000	28.100000	...	53.200000	60.600000	38.100000	56.90000	12.000000	26.600000	31.000000	93.300000	45.400000	68.600000

8 rows × 24 columns

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36840 entries, 0 to 36839
Data columns (total 14 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   EU_circo                36840 non-null  object 
 1   code_région             36840 non-null  int64  
 2   nom_région              36840 non-null  object 
 3   chef.lieu_région        36840 non-null  object 
 4   numéro_département      36840 non-null  object 
 5   nom_département         36840 non-null  object 
 6   préfecture              36840 non-null  object 
 7   numéro_circonscription  36840 non-null  int64  
 8   nom_commune             36840 non-null  object 
 9   codes_postaux           36840 non-null  object 
 10  code_insee              36840 non-null  int64  
 11  latitude                33911 non-null  float64
 12  longitude               33999 non-null  object 
 13  éloignement             33878 non-null  float64
dtypes: float64(2), int64(3), object(9)
memory usage: 3.9+ MB

3. Data analysis

3.1. Current French labour market information

3.1.1. The unemployment rate

In Q1 2021 the unemployment rate in France has reached 8.1%.

3.1.2. Mean salary for France

The average net salary per hour for the whole country currently achieved: 13.71€. For womens it is 12.04€, and for mens 14.85€

3.1.3. Mean salary by working position

3.1.4. Mean salary by age

Net payment is starting to grow with the age. The discrepancies between gender payments are getting more visible with the age.

3.2. Labour situation by regions

3.2.1. Mean salary per region

Some of France Regions have been merged during last period. Data have been updated to reflect those changes and new Region names.

	code_insee	EU_circo	code_région	nom_région	chef.lieu_région
0	01024	Sud-Est	82	Rhône-Alpes	Lyon
1	01029	Sud-Est	82	Rhône-Alpes	Lyon
2	01038	Sud-Est	82	Rhône-Alpes	Lyon
3	01040	Sud-Est	82	Rhône-Alpes	Lyon
4	01245	Sud-Est	82	Rhône-Alpes	Lyon
...	...	...	...	...	...
36835	97613	Outre-Mer	5	Mayotte	Mamoudzou
36836	97614	Outre-Mer	5	Mayotte	Mamoudzou
36837	97615	Outre-Mer	5	Mayotte	Mamoudzou
36838	97616	Outre-Mer	5	Mayotte	Mamoudzou
36839	97617	Outre-Mer	5	Mayotte	Mamoudzou

36693 rows × 5 columns

3.2.2. Regions with the lowest and highest salaries

3.2.3. Salary per region and gender

3.3. Labour situation by cities

3.3.1. Mean salary per town

3.3.2. Mean salary per town and gender

In the whole country there are only two towns where is noted higher average payment for women than men. However, the difference is slight.

3.3.3. Mean salary per town and age

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